k折交叉验证matlab代码

下面是一个使用MATLAB实现k折交叉验证的代码示例:

[M, N] = size(data); % 数据集为一个M*N的矩阵，其中每一行代表一个样本
indices = crossvalind('Kfold', data(1:M, N), k); % 进行随机分包

for fold = 1:k
    test_indices = (indices == fold); % 获得当前测试集元素在数据集中对应的单元编号
    train_indices = ~test_indices; % 获得当前训练集元素在数据集中对应的单元编号

    train_data = data(train_indices, :); % 从数据集中划分出训练样本的数据
    train_target = target(:, train_indices); % 获得训练样本集的目标变量

    test_data = data(test_indices, :); % 获得测试样本集的数据
    test_target = target(:, test_indices); % 获得测试样本集的目标变量

    [HammingLoss(fold), RankingLoss(fold), OneError(fold), Coverage(fold), Average_Precision(fold), Outputs, Pre_Labels.MLKNN] = MLKNN_algorithm(train_data, train_target, test_data, test_target); % 要验证的算法

end

上述代码将数据集`data`随机分为k个包，每次将其中一个包作为测试集，剩下的k-1个包作为训练集进行训练。在每一次迭代中，我们可以获取到一系列的验证指标（如Hamming Loss、Ranking Loss等）以及MLKNN算法的输出结果。<span class="em">1</span><span class="em">2</span>
#### 引用[.reference_title]
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